计算机工程

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基于显著性与弱凸性的三维点云模型分割

郑乐乐,韩慧妍,韩燮   

  1. (中北大学 计算机与控制工程学院,太原 030051)
  • 收稿日期:2017-08-14 出版日期:2018-04-15 发布日期:2018-04-15
  • 作者简介:郑乐乐(1992—),男,硕士研究生,主研方向为计算机仿真与可视化、虚拟现实;韩慧妍,讲师;韩燮,教授。
  • 基金项目:
    国家自然科学基金(61672473);山西省自然科学基金(2015021093);山西省回国留学人员科研项目(2015-079)。

Three-dimensional Point Cloud Model Segmentation Based on Significance and Weak Convexity

ZHENG Lele,HAN Huiyan,HAN Xie   

  1. Three-dimensional Point Cloud Model Segmentation Based on Significance and Weak Convexity
  • Received:2017-08-14 Online:2018-04-15 Published:2018-04-15

摘要: 针对现有三维点云模型分割算法无法同时分割出大小组件的问题,提出一种基于显著性和弱凸性的分割方法。根据谱聚类方法将点云模型过分割为弱凸块,在此基础上,利用显著性判定提取较小的突出部分和面积较小但边缘特征点明显的弱凸块,解决欠分割问题,最终从相互可见性和体积相似性角度进行区域合,解决过分割问题。实验结果表明,该算法的分割结果优于Heterogeneous、Constraint Planar等无监督方法。

关键词: 弱凸性, 显著性, 谱聚类, 相互可见性, 体积相似性

Abstract: The existing three-dimensional point cloud model segmentation algorithms cannot segment large components and small components at the same time.Aiming at this problem,in this paper,a segmentation method is proposed based on the significance and weak convexity.Firstly,the point cloud model is segmented into weak bumps by spectral clustering method.On this basis,the significance test is used for extracting smaller projections and the weak bumps with small area but the edge feature points are obvious,to solve the problem of under-segmentation.Finally,through mutual visibility and volume similarity,the region is merged to solve the problem of over-segmentation.Experimental results show that the segmentation results of the proposed method are better than those of unsupervised methods such as Heterogeneous,Constraint Planar.

Key words: weak convexity, significance, spectral clustering, mutual visibility, volume similarity

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